Feature selection and classification method based on maximum information coefficient and feature selection and classification device based on maximum information coefficient

Abstract

The invention relates to a feature selection and classification method based on a maximum information coefficient and a feature selection and classification device based on the maximum information coefficient. The feature selection method comprises the following steps: S1, sequencing primitive features based on mutual information criterions, deleting features with correlation degree lower than a threshold value, and forming a primary feature subset by use of features with correlation degree higher than the threshold value; S2, calculating the maximum information coefficient among the features in the primary feature subset; S3, deleting redundancy features in the primitive feature subset according to the maximum information coefficient, thereby obtaining a low-dimension feature subset. According to the feature selection method, feature selection is performed by virtue of a mode of using mutual information and the maximum information coefficient, so that the redundancy features are removed, and the data dimension is reduced.

Description

technical field [0001] The present invention relates to the field of wireless communication, in particular to a feature selection and classification method and device based on maximum information coefficient. Background technique [0002] With the development of science and technology, the scale of data has also increased rapidly, and intelligent analysis and processing of these data has become more and more important. But the ensuing problem is that there is a lot of redundant information in the massive raw data, which poses challenges to the algorithms in the fields of pattern recognition and machine learning. On the one hand, redundant data greatly increases the time complexity and space complexity of the algorithm, wastes storage resources, and increases processing time; on the other hand, due to the large correlation of redundant data, the classification results are biased. Reduced performance of intelligent information processing software. The purpose of feature sele...

AI Technical Summary

Problems solved by technology

However, most of the traditional feature subset selection methods select features based on the linear correlation between features, and it is difficult to eliminate the ubiquitous nonlinear redundancy, which limits the performance improvement of this type of method.

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